| Full text | |
| Author(s): Show less - |
Tonussi, Rafael Lara
[1]
;
de Oliveira Silva, Rafael Medeiros
[1]
;
Braga Magalhaes, Ana Fabricia
[1]
;
Espigolan, Rafael
[1]
;
Peripolli, Elisa
[1]
;
Olivieri, Bianca Ferreira
[1]
;
Braga Feitosal, Fabieli Loise
[1]
;
Antunes Lemos, Marcos Vinicfus
[1]
;
Berton, Mariana Piatto
[1]
;
Justino Chiaia, Hermenegildo Lucas
[1]
;
Cravo Pereira, Angelica Simone
[2]
;
Lobo, Raysildo Barbosa
[3]
;
Framartino Bezerra, Luiz Antonio
[4]
;
Magnabosco, Claudio de Ulhoa
[5]
;
Lourenco, Daniela Andressa Lino
[6]
;
Aguilar, Ignacio
[7]
;
Baldi, Fernando
[1]
Total Authors: 17
|
| Affiliation: | [1] Sch Agr & Veterinarian Sci, Dept Anim Sci, Sao Paulo - Brazil
[2] Fac Anim Sci & Food Engn, Dept Nutr & Anim Prod, Pirassununga - Brazil
[3] Natl Assoc Breeders & Researchers ANCP, Ribeirao Preto - Brazil
[4] Med Sch Ribeirao Preto, Dept Genet, Ribeirao Preto - Brazil
[5] Brazilian Agr Res Corp EMBRAPA, Brasilia, DF - Brazil
[6] Univ Georgia, Dept Anim & Dairy Sci, Athens, GA 30602 - USA
[7] Natl Inst Agr Res, Dept Anim Breeding, Las Brujas - Uruguay
Total Affiliations: 7
|
| Document type: | Journal article |
| Source: | PLoS One; v. 12, n. 9 SEP 28 2017. |
| Web of Science Citations: | 5 |
| Abstract | |
The objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A(22) matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A(22)) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the population increased. The EBV accuracy reduction was higher for GEN and YOUNG groups. By analyzing the scenarios for YOUNG (from 0 to 100% of MS), the decrease was 87.8 and 86% for AFC and W550, respectively. When applying the ssGBLUP model, the accuracies of genetic evaluation also decreased as the MS in the pedigree for both traits increased. However, the accuracy reduction was less than those observed for BLUP model. Using the same comparison (scenario 0 to 100% of MS), the accuracies reductions were 38 and 44.6% for AFC and W550, respectively. There were no differences between the strategies for scaling the G matrix for ALL, BULL, and FEM groups under the different scenarios with missing pedigree. These results pointed out that the uninformative part of the A(22) matrix and genotyped animals with paternity uncertainty did not influence the scaling of G matrix. On the basis of the results, it is important to have a G matrix in the same scale of the A(22) matrix, especially for the evaluation of young animals in situations with missing pedigree information. In these situations, the ssGBLUP model is an appropriate alternative to obtain a more reliable and less biased estimate of breeding values, especially for young animals with few or no phenotypic records. For accurate and unbiased genomic predictions with ssGBLUP, it is necessary to assure that the G matrix is compatible with the A(22) matrix, even in situations with paternity uncertainty. (AU) | |
| FAPESP's process: | 11/21241-0 - Study of the genetic variability of meat fatty acid profile in Nelore cattle finished in feedlot |
| Grantee: | Fernando Sebastián Baldi Rey |
| Support Opportunities: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 13/25910-0 - Assessment strategies genotyping in situations of uncertainty of paternity and its impact on the evaluation genomic in beef cattle |
| Grantee: | Rafael Lara Tonussi |
| Support Opportunities: | Scholarships in Brazil - Doctorate |